The influence of task environment and health literacy on the quality of parent-reported ADHD data
Bibliographic record
Abstract
OBJECTIVES: To determine 1) the extent to which paper-based and computer-based environments influence the sufficiency of parents' report of child behaviors and the accuracy of data on current medications, and 2) the impact of parents' health literacy on the quality of information produced. METHODS: We completed a randomized controlled trial of data entry tasks with parents of children with Attention Deficit Hyperactivity Disorder (ADHD). Parents completed the NICHQ Vanderbilt ADHD screen and a report of current ADHD medications on paper or using a computer application designed to facilitate data entry. Literacy was assessed by the Test of Functional Health Literacy in Adults (TOFHLA). Primary outcomes included sufficient data to screen for ADHD subtypes and accurate report of total daily dose of prescribed ADHD medications. RESULTS: Of 271 parents screened, 194/271 were eligible and 182 were randomized. Data from 180 parents were analyzed. 5.6% parents had inadequate/marginal TOFHLA scores. Using the computer, parents provided more sufficient and accurate data compared to paper (sufficiency for ADHD screening, paper vs. computer: 87.8% vs. 93.3%, P = 0.20; accuracy of medication report: 14.3% vs. 69.4%; p<0.0001). Parents with adequate literacy had increased odds of reporting sufficient and accurate data (sufficiency for ADHD screening: OR 8.0, 95% CI 2.0-32.1; accuracy of medication report: OR 4.4, 95% CI 0.5-37.4). In adjusted models, the computer task environment remained a significant predictor of accurate medication report (OR 18.7, 95% CI 7.5-46.9). CONCLUSIONS: Structured, computer-based data entry by parents may improve the quality of specific types of information needed for ADHD care. Health literacy affects parents' ability to share valid information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".